Browsing by Subject "computational modeling"
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Item Design and application of a proxy system model for the quantitative reconstruction of hydroclimate variabiility recorded by oxygen isotopes in lacustrine carbonate sediment(2021-01) Fernandez, AlejandroOxygen isotope analyses of lacustrine sediment, which are widely used as proxies of past climatic variability, have become increasingly reliant on computational modeling approaches that allow for quantitative interpretations of past hydroclimate, constraining of water resources’ sensitivities to changes in climate, and direct comparisons of proxy data with climate models. In this study, we present the development, structure and application of a Proxy System Model (PSM) designed for Castor Lake (Washington, U.S.A): a well-understood, highly monitored small lake system. The principal goal is to improve upon the understanding of the relationships between climate and the stable oxygen isotope (18O) proxy system in the context of lake sediments, by addressing the impacts that a variety of climate variables, as well as non-climate relate factors such as basin morphology, vegetation, hydrologic setting and lake mixing, have on the isotopic signatures of resulting sediments in the lake, as well as to provide a quantitative basis from which well-informed reconstructions of past climate can be made. Following a calibration process based on over a century of compiled daily weather data as well as approximately 14 years of in-situ continuous measurements of lake level, temperature and water oxygen isotope samples, the PSM was shown to accurately reproduce seasonal, interannual and century-scale trends of sediment oxygen isotope values and water balance, with varying degrees of accuracy for different timescales. Model-based reconstructions of hydroclimate variables for an early Holocene (~10000 years B.P.) δ18O maximum in the Castor Lake sediment record show that cold-season (i.e. winter) precipitation and relative humidity must have been lower by 21% ± 5% and 14% ± 7%,respectively, in order for the observed sediment δ18O signature to be produced and recorded. Furthermore, air temperature and warm-season precipitations seem to be negligible controls on sediment δ18O signatures, opposite to what was expected following the temperature dependence of carbonate sediment formation and isotopic fractionation. These results showcase the advantages of the application of PSMs to the analysis of paleoclimate proxy records as a way to make well-informed quantitative interpretations of past climate change through the constraining of physical, chemical and biological processes that impact the formation of the sedimentary archive.Item Failure Mechanics of Nonlinear, Heterogeneous, Anisotropic Cardiovascular Tissues: Implications for Ascending Thoracic Aortic Aneurysms(2019-06) Korenczuk, Christopher E.Characterizing the mechanical response and failure mechanisms of cardiovascular tissues is critically important, as these tissues play a vital role in the native functioning of the body. In the case of pathological events, such as aortic aneurysms or myocardial infarctions, mechanical behavior can be altered due to adverse remodeling, and thus affect the integrity of the tissue. Ascending thoracic aortic aneurysms (ATAAs) occur when the aorta enlarges beyond its normal diameter, and dilation is typically accompanied by disorganization of the underlying aortic fibrous structure. Current diagnostic methods depend solely on measuring aneurysm diameter, neglecting considerations of mechanical strength, which results in an inefficient risk assessment. To better understand the failure mechanism of ATAAs, the work presented here used a combination of experimental testing and computational modeling to characterize failure in human ATAA tissue. Experimental testing showed that ATAA tissue exhibited significantly lower mechanical strength when compared to healthy porcine tissue in multiple loading configurations. Furthermore, experimental tests highlighted the large disparity between uniaxial and shear strength in ATAA tissue, where the tissue was substantially weaker in shear loading conditions. A custom multiscale finite-element model was then used to interrogate fiber failure more closely in both experimental loading conditions, and inflation of a patient-specific ATAA geometry. Modeling results showed that fibers between the lamellar layers of the aortic wall failed significantly more than fibers within the planar layers in shear loading conditions, as well as during inflation of the patient-specific geometry. Taken together, these results suggest that intramural shear could be an important contributor to the failure or dissection of ATAAs.Item How anatomical details affect noninvasive brain stimulation in computational models(2023-01) Mantell, KathleenNoninvasive brain stimulation (NIBS) is an exciting field of study that is becoming increasingly popular for its many therapeutic uses. Two of the most widely used types of NIBS are transcranial electric and magnetic stimulation (TES and TMS). NIBS takes advantage of the electrical properties of neurons by modifying neuronal behavior through externally applied electric fields. This is achieved by either passing a current through two or more electrodes (TES) or inducing electric fields via a time varying magnetic field (TMS). Today, the biggest problems facing the NIBS field are the variability of responses in experiments and clinical settings and translating findings from animal studies to humans. To work to address these problems, we employ the power of computational modeling, specifically finite element method (FEM) modeling. FEM modeling allows us to build head models and simulate TMS and TES induced electric fields. However, there are many factors that go into building accurate models and it is not always clear how important they are in estimating the NIBS induced electric fields. Therefore, in this dissertation I explain how we look at three different factors in building FEM models: inclusion of stroke lesions in pediatric models, changing head model size, and inclusion of muscle tissue. In this work we found that stroke lesions greatly influence variability of the TMS induced electric field, either increasing or decreasing the electric field strength depending on the TMS coil location. This indicates that individualized head models are key to planning future experiments because the complex morphology does not allow us to make a simple prediction about the electric field. Next, we found that head size plays a significant role in NIBS induced electric fields, both in spherical models and non-human primate (NHP) models. For TES the electric field strength exponentially decreases with increasing head size. But the TMS induced electric field strength first increases with head size and then decreases after a critical point based on the TMS coil size. Finally, we determined that muscle tissue is an important feature in NHP models for TES simulations and it increases the electric field strength, but the percent change can be influenced by anisotropic properties of the muscle. Overall, these results from modeling nonstandard cases suggest that individualized modeling with careful consideration of the model setup is vital to accurately predicting NIBS induced electric fields.Item Microtubule-based control of glioma cell migration mechanics(2018-08) Prahl, LouisCell migration underlies the extensive tissue invasion that drives brain tumor (glioma) progression. Glioma cell migration involves the coordinated mechanical functions of the actin cytoskeleton, myosin motors, and substrate adhesions through a biophysical motor-clutch model. Although computational forms of the motor-clutch model predict glioma cell migration behaviors as a function of tissue stiffness, less is known about how other cellular structures such as microtubules influence migration. Presently, a number of microtubule-targeting agents (MTAs) are used to treat various cancers (including gliomas) so understanding their mechanism of action is necessary in order to develop better therapies. In this dissertation, I show that two commonly used MTAs (paclitaxel and vinblastine) each have distinct and nearly opposite effects on traction forces that motor-clutch simulations predict, and which correlate with changes to microtubule organization and dynamics. Effects of MTAs are consistent with influencing F-actin assembly and nucleation rates of protrusions, which impairs the ability of glioma cells to spontaneously polarize and migrate. Microtubule-dependent signaling networks that are perturbed in MTA-treated cells support novel roles for receptor tyrosine kinase (RTK) signaling pathways in mediating these effects. In the final study, we use microfabricated channels that replicate geometric and mechanical features of brain tissue alongside simulation-based methods to study confined glioma cell migration. Simulations recapitulate the dynamics of glioma cell migration in microchannels, as well as accurate predictions of the effects of MTAs and other pharmacological inhibitors of motor-clutch system components. This provides novel evidence for motor-clutch-based cell migration in confinement. In summary, this dissertation identifies specific mechanisms by which microtubules regulate motor-clutch based migration of glioma cells, and outlines a systems-level physics-based approach for understanding anti-motility therapy.Item Personalized computational models of deep brain stimulation(2016-12) Teplitzky, BenDeep brain stimulation (DBS) therapy is used for managing symptoms associated with a growing number of neurological disorders. One of the primary challenges with delivering this therapy, however, continues to be accurate neurosurgical targeting of the DBS lead electrodes and post-operative programming of the stimulation settings. Two approaches for addressing targeting have been advanced in recent years. These include novel DBS lead designs with more electrodes and computational models that can predict cellular modulation during DBS. Here, we developed a personalized computational modeling framework to (1) thoroughly investigate the electrode design parameter space for current and future DBS array designs, (2) generate and evaluate machine learning feature sets for semi-automated programming of DBS arrays, (3) study the influence of model parameters in predicting behavioral and electrophysiological outcomes of DBS in a preclinical animal model of Parkinson’s disease, and (4) evaluate feasibility of a novel endovascular targeting approach to delivering DBS therapy in humans. These studies show how independent current controlled stimulation with advanced machine learning algorithms can negate the need for highly dense electrode arrays to shift, steer, and sculpt regions of modulation within the brain. Additionally, these studies show that while advanced and personalized computational models of DBS can predict many of the behavioral and electrophysiological outcomes of DBS, there are remaining inconsistencies that suggest there are additional physiological mechanisms of DBS that are not yet well understood. Finally, the results show how computational models can be beneficial for prospective development of novel approaches to neuromodulation prior to large-scale preclinical and clinical studies.Item Supporting Data for "From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains"(2020-07-06) Shen, Zhengyuan; Chen, Jingyi L; Vernadskaia, Viktoriia; Ertem, S Piril; Mahanthappa, Mahesh K; Hillmyer, Marc A; Reineke, Theresa M; Lodge, Timothy P; Siepmann, J Ilja; siepmann@umn.edu; Siepmann, J Ilja; Materials Research Science & Engineering Center (MRSEC)Data including input/output and restart files for all the systems, analysis codes (python, fortran, cpp), and figures in the paper "From Order to Disorder: Computational Design of Triblock Amphiphiles with 1 nm Domains." Sample molecular dynamics trajectories pieces are provided due to the extremely long simulation trajectories.Item Time allocation and meta-cognition: A computational approach towards the organization of motivation(2019-01) Mussack, DominicHow do people allocate time and effort across tasks? This dissertation takes a computational psychology perspective, and puts forward the theory that motivation solves the meta-cognitive problem of allocating resources to different tasks by computing task priority. Motivation research has previously distinguished between two dissociable components of motivation: directing and energizing. These two components refer to different resources that must be allocated: time and effort. We explore the way humans allocate effort by taking advantage of simple decision making tasks and manipulating either task or background information. We develop a novel method that allows researchers to integrate an array of biometrics that capture how decision processes are modulated. We then extend work from optimal foraging theory to account for human tasks in order to analyze how humans allocate time. We derive various results that match time use behavior across domains. Finally, we apply the structural implications of this theory to make predictions in large scale time use data sets. Humans must often schedule mutually exclusive goals to fulfill mutually exclusive needs, which requires us to allocate time across tasks via a priority computation. Re-framing motivation from a resource allocation perspective, and highlighting the unique problem of time allocation, has implications across human decision making behavior, and we demonstrate its relevance in multiple domains.Item Understanding the Glomerular Mesangium through computational modeling(2017-06) Hunt, SarahThe mesangium plays a prominent role in maintaining glomerular homeostasis by contributing to hemodynamic regulation, macromolecule clearance, and immune monitoring. However, it is also intimately involved in the development of glomerular disease. In this work we examine the physics of transport in the mesangial region by creating a computational model. This model suggests that physiological parameters play a key role in controlling the distribution of macromolecules within the mesangium. In particular, it suggests that aberrant glycosylation of IgA in IgA nephropathy may be damaging because of how it changes the Péclet number. The model is then extended to describe transport within the glomerular tuft through the mesangial matrix. Again, we examine this transport under a range of physiological parameters. Our results suggest that transport within the mesangium may operate as one of two broad regimes – an “accumulating” regime where the mesangium provides additional filtration surface area and large macromolecules may accumulate in the region, and a “shunting” regime where the mesangium allows solutes to bypass the full length of glomerular capillary filtration.